This approach relies on the ability to calculate both the prior of the parameters \(p(\theta )\) , which is generally tractable, and the likelihood \(p(x|\theta )\) . However in many models of interest it is not tractable or not possible to directly evaluate \(p(x|\theta )\) , for example in population genetics [1]}, random graph models [2]} and some models of dynamical systems [3]}, [4]}. To allow us to perform Bayesian inference in these situations, an approach named Approximate Bayesian Computation (ABC) was developed, based on initial work in [5]} and [6]}, developed further in [1]} and [8]}, and expanded in many works, see e.g. [9]}, [3]}, [11]}, [12]}, [13]}.
